1. Field of the Invention
The present invention relates to a method for reducing image noise, and more specifically, to a method for reducing image noise through calculating a mean pixel-correlation between a central pixel and its neighboring pixels, selecting an appropriate working window to reduce image noise, and calculating a weighted horizontal similarity between the central pixel and the horizontal neighboring pixels and a weighted vertical similarity between the central pixel and the vertical neighboring pixels.
2. Description of the Prior Art
Image information plays a very important role in multimedia communications today. However, no image is perfect due to image noise.
Principal sources of noise in digital images arise during image acquisition (digitization) and/or transmission. Performance of imaging sensors is affected by a variety of factors, such as environmental conditions during image acquisition, and by the quality of the sensing elements themselves. For instance, in acquiring images with a CCD camera, luminosity and sensor temperature are major factors affecting the amount of noise present in the generated images. Images are corrupted during transmission principally due to interference in channels used for transmission. For example, an image transmitted through a wireless network might be disturbed as a result of lightning or other atmospheric charged particles.
Filtering a digital image is one necessary part of image processing, and is used for reducing noise while protecting image details. For example, any noise in images will result in serious errors due to many applications being based on operands drawn out from applications for calculating images. Therefore, filtering methods for reducing noise are desired not only to improve the visual quality, but also to improve the performance of subsequent processing tasks, such as coding, analyzing, segmenting, recognition, or interpretation.
In digital images, image pixels usually experience interference from impulse noise due to wrong image acquisition equipment, poor image acquisition conditions, or errors in image transmission. Impulse noise is discovered easily by human eyes and causes serious mistakes in image processing applications. Hence, impulse noise reduction is used for front-end processing in some image processing systems, such as image quantification.
A best impulse noise filter must have capacity to smooth non-similar pixels in identical areas to retain edge information, and not change any natural image information. Different impulse noise reduction algorithms have already been disclosed in previous years, their purposes being to filter impulse noise and to keep image detail at the same time. Some typical non-linear filters, such as median filters and weighted median filters, are used for reducing almost all impulse noise and keeping almost all image detail.
Applications of reducing image noise have already been disclosed in the prior art. For example, a differential rank impulse detector (DRID) is provided for detecting impulse noise effectively.
In a working window, the difference between the arrangement sequence of impulse noise and the arrangement sequence of a center pixel is very large. The median values in different sequences always lie in the middle, but the median value of the impulse noise lies near two extremities. A simple impulse noise detector can be obtained for this reason. Its concept is comparing the location of an interested pixel with a threshold, and can be expressed in the following equation:(R(Xi,j)≦s)(R(Xi,j))≧N−s+1;
wherein Xi,j is a center pixel of a working window, R(Xi,j) is a rank after sorting, N is a number of pixels in the working window, and s is a threshold value.
It is easy to determine noise interference and to get a great reduction effect through this method, but there are many erroneous judgments, and whether a pixel experiences interference from noise is not guaranteed. A pixel will be regarded as noise if it does not experience interference from noise and lies near the two extremities. In order to overcome this problem, not only the sorting sequence, but also the grayscale value, should be considered. The algorithm can be expressed as another equation.(R(Xi,j)≦s)(R(Xi,j)≧(N−s+1))(di,j≧θ);
wherein, di,j can be expressed as:
      d          i      ,      j        ≡      {                                                                                                      x                                      i                    ,                    j                                                  -                                  Var                  ⁡                                      [                                                                  R                        ⁡                                                  (                                                      x                                                          i                              ,                              j                                                                                )                                                                    -                      1                                        ]                                                                                      ,                                                              if              ⁢                                                          ⁢                              R                ⁡                                  (                                      x                                          i                      ,                      j                                                        )                                                      >                          MED                              i                ,                j                                                                                                                                                    x                                      i                    ,                    j                                                  -                                  Var                  ⁡                                      [                                                                  R                        ⁡                                                  (                                                      x                                                          i                              ,                              j                                                                                )                                                                    +                      1                                        ]                                                                                      ,                                                              if              ⁢                                                          ⁢                              R                ⁡                                  (                                      x                                          i                      ,                      j                                                        )                                                      <                          MED                              i                ,                j                                                                                      0            ,                                    else                         
Var (k) is the grayscale value of a sorting k. The detector provides an effective and fast method based on comparing the locations of the pixels within the working window with an absolute value. There is no smooth image in this method, and this method can be applied to any other filter.
In the prior art, a conditional signal-adaptive median filter (CSAM) is provided to reduce image noise. The CSAM filter is a median filter based on judgments. The filter consists of two primary functions: necessary conditions for determination, and a method for filtering noise. The first function is used for determining whether noise exists in a working window, and the second function is used for smoothing a pixel value of the noise.
The algorithm is expressed as the following:
Step 1: Calculate upper limits and lower limits in identical areas.
Step 2: Detect impulse noise.
In a 3×3 working window, let a center pixel be x0, 8 neighboring pixels be xi|i=18, ch be an identical number of pixels among the center pixel x0 and the 8 neighboring pixels, and ci be a non-identical number of pixels among the center pixel x0 and the 8 neighboring pixels. The center pixel x0 is determined as a signal if the value ch is greater than the value ci, and is determined as a noise candidate if the value ch is less than the value ci.
Step 3: Refine the selected impulse noise.
A different filtering method is utilized to remove pixels not experiencing interference from noise from the noise candidate set to decrease error detections. Erroneous detected pixels mostly lie near edges or in image details.
Those pixels are divided into two groups: one group similar to the center pixel, and the other group not similar to the center pixel. The center pixel is determined as a signal and is removed from the noise candidate set if the number of pixels that are similar to the center pixel is greater than the number of pixels that are not similar to the center pixel. The step is executed continuously until the number of the noise candidate set does not decrease anymore.
Step 4: Use a median filter to reduce noise.
A 3×3 median filter is used to reduce noise in a 3×3 working window if the number of pixels that are similar to the center pixel is less than 3. Otherwise, a 5×5 median filter is used to reduce noise.
The objective of the method is to reach perfect impulse noise detection and to keep superior visible quality after restoration.
Furthermore, a Truncation filter can also be utilized to reduce image noise, wherein x(i,j) represents a grayscale of a pixel (i,j), and N square windows of M×M size having the pixel (i,j) can be found. This kind of window is called an internal window and is expressed as WIk.
For each internal window, a corresponding external window WOk of (M+2r)×(M+2r) size can be found, wherein r≧1. The internal window WIk and the corresponding external window WOk have the same center. In such a manner, N close surrounding bands BK having a thickness r can be found (wherein K=1 . . . N). The close surrounding bands BK are defined as BK=WOk−WIk. Suppose uk and vk represent the maximum grayscale and the minimum grayscale in each close surrounding band BK. The maximum grayscale and the minimum grayscale of its surrounding groups are used for determining whether noise interference exists. The objective of this method is to protect image details when reducing noise.
It should be mentioned that an adaptive two-pass median filtering (ATPMF) can also be utilized to reduce image noise. Sorting filters, such as median filters, may result in poor performance when the noise ratio is high. Proceeding with this kind of filter twice achieves better performance, hence the name “two-pass.”
This method achieves two goals. First, more noise can be reduced by utilizing this two-pass median filtering algorithm than a general median filtering one when the noise ratio is high. Next, estimated space distribution of the impulse noise is utilized to correct errors resulting from the first filtering. The concept of this method is described in the following.
Step 1: Obtain the estimated space distribution and the impulse noise values by utilizing a median filter to reduce image noise.
Step 2: Determine which pixels are over-corrected after reducing noise in step 1, and use original pixel values to replace these pixel values and keep these values as constant values in step 3.
Step 3: Use the median filter to reduce image noise again.
The objective of this method is reducing image noise in an image that experiences interference from impulse noise of a high noise ratio, and the method can be applied to any sorting filter.
Thus it can be seen that numerous image noise reduction algorithms have already been disclosed in the prior art. However, in some algorithms, only images that are interfered by impulse noise of a high noise ratio are suitable for use. And in some conditions, erroneous judgment may happen. Moreover, both reducing noise effectively and protecting image detail should be a concern.